Verification

step · paper open

Declaration 8f199958cdfb has not been accepted by anyone. · no paper read under it

Did anyone check, and what did they find?

Mechanical: re-runnable from its declared inputs.

Part of Measures — What can be measured over the tree - its coverage, its wording, whether its code re-runs?

How it works

Runs the paper's own deposited script under observation and records what it actually did — every path it opened, every value it computed — rather than what its output said it did. Nothing is taken from the script's own account of itself, because that is precisely what failed: the first script audited had a claim recorded verified while the function named in its record raised a shape-mismatch error, and a reproduction record naming a file the code never opened.

This is not replication. It re-runs the authors' code on the authors' data, so it can catch a record that does not match its own run and it cannot tell you whether the finding is true.

What it found · added 2026-09-06

133 of 151 claims that a re-run could settle carry a reproduction record, and their outcomes are not uniform. Observing the runs from outside also showed that most records were reached by reading a deposit rather than by running the paper's own script.

Rests on

Feeds — a change here disturbs these

How it is defined

What this layer reads besides its dependencies. Each is a declared input: its content is hashed into every run, so editing one makes those runs stale.

verification/{paper}/verify.pythe script that runs itnot in the repository

The declaration names this path and the repository does not have it. An input that does not exist hashes to nothing, so it cannot make a run stale — the layer is declared to depend on something it is not in fact tracking.

verification/audit_run.pythe script that runs it416 lines

What it says it does

Run a verification script under observation, and record what it actually did.

Only one of the nine verification scripts was ever audited, and auditing it found two
failures that no amount of reading would have caught:

  * a claim recorded `verified` while the function named in its record raised
    `shapes (4,4) and (5,5) not aligned` -- the verdict had been narrated, not observed;
  * a reproduction record naming `fMRI - Choices_singleTrialData.csv` while the code opened
    `Behav - Choices_singleTrialData.csv`.

Both were found by making the script report every path it opened and every value it computed.
That was done for Gaedeke by editing the script to call `used()` at each open. Doing the same
to eight more scripts would mean eight sets of hand-edits, each an opportunity to annotate a
path the code does not take -- which is the very failure being audited.

So this observes from outside instead. It patches `open` and the common loaders, executes the
script in-process, and reads its `ROWS` list afterwards. Nothing is taken from the script's
own account of itself: the file list comes from the file system calls, the results come from
the list the printed table is built from, and an exception is recorded whether or not the
script caught it.

What it emits, beside the script it ran:

  verification/<paper>/provenance.json    files opened, results produced, exceptions raised

Usage:
  python3 verification/audit_run.py <paper-slug> [-- script args]
  python3 verification/audit_run.py --all
  python3 verification/audit_run.py --all --timeout 1800

`--timeout` now defaults to 1800s rather than to no limit. The documented example used to say
900, which is shorter than the slowest verification script's own internal budget: Ejdrup's fast
mode runs two figure scripts and allows each 600s, so its worst case is 1200s before the clone
is counted, and an uncontended run measured 1064s. A run killed at 900s writes no results, and
`audit_verifications` then reports the paper as a failed run — which it was not. The script had
been working the whole time and the observer was giving up first.

A default that cannot accommodate the slowest thing it observes is a fault in the observer, and
one that reads as a fault in the observed, which is the worst way for it to be wrong.

What it produces5 results

One paper, as the worked example — Bouyeure, v1. Read from verification/bouyeure-2026-fear-rsa/provenance.json · 5 KB. script verification/bouyeure-2026-fear-rsa/verify.pyobserved_by verification/audit_run.pyrecorded 2026-09-13T15:43:58+00:00exit SystemExit(0)

# claimpaper_valuereproduced_valuestatusmeasured
1 cs-plus-univariate-fear-network-acquisition dACC/SFG cluster confirmed, >100 total sig voxels total=5544 voxels, dACC/SFG=1075, peak=[6.0, 15.0, 39.0] PASS true
2 cue-generalization-increases-acquisition 2283 sig voxels, peak -log10(p)=2.959 in dACC/SFG n_sig=2283, peak=[-11.5, 10.0, 44.0], peak_val=2.959 PASS true
3 current-threat-activates-fear-network-reversal >1000 sig voxels in fear network total=7473 voxels, peak=[-9.0, 10.0, 41.5] PASS true
4 prior-threat-activates-fear-network-weakly ~36 sig voxels, occipital peak NOT fear network (MISMATCH documented) n_sig=36, peak=[-9.0, -92.5, -6.0] PASS true
5 behavioral-learning-confirms-contingencies CS++ > CS+- > CS-+ > CS--, p<0.0001 Download failed WARN true

Across the corpus

3 not run · 7 run not observed·a paper links to its own cell, where this layer's output for it is rendered

PaperStateVersionLast runOutputCell
A three-dimensional immunofluorescence atlas of the …not run———json
Distinct representational properties of cues and con…backfilled from the artifact on diskrun not observedv12026-09-10provenance.jsonjson
Computational modelling identifies key determinants …backfilled from the artifact on diskrun not observedv12026-09-10provenance.jsonjson
Contributions of insula and superior temporal sulcus…backfilled from the artifact on diskrun not observedv12026-09-11provenance.jsonjson
Spatially targeted inhibitory rhythms differentially…backfilled from the artifact on diskrun not observedv12026-09-10provenance.jsonjson
Feedback of peripheral saccade targets to early fove…not run———json
iGABASnFR2 is an improved genetically encoded protei…backfilled from the artifact on diskrun not observedv12026-09-10provenance.jsonjson
A deep learning pipeline for mapping in situ network…not run———json
Self-association enhances early attentional selectio…backfilled from the artifact on diskrun not observedv12026-09-10provenance.jsonjson
Impaired excitability of fast-spiking neurons in a n…backfilled from the artifact on diskrun not observedv12026-09-10provenance.jsonjson

Inputs and outputs

Reads, besides its dependencies
Produces
  • verification/{paper}/provenance.json

One per paper — the table above links each one that exists.

Views
  • graph — on the paper page, as the claim graph
  • table — rendered above, over the 5 results in the artifact
  • comparison — on the cell page, two versions aligned by the matcher, wherever the ledger holds more than one

Running it

The command comes from the declaration, so this text and what actually runs cannot diverge. pipeline.py run also runs the unmet dependencies first.

python3 scripts/pipeline.py run <paper> verification

Underneath, that runs python3 verification/audit_run.py {paper}.